# Machine Learning Meetup Notes: 2010-04-21

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*Talked about gradient descent | *Talked about gradient descent | ||

*Passed around some python code for doing least squares | *Passed around some python code for doing least squares | ||

+ | *Talked about starting a linear algebra mini-course | ||

+ | *Talked about presenting stuff on SVMs at next meetup | ||

=== Details === | === Details === | ||

− | *Some good books on linear regression: | + | *Some good books and references on linear regression/machine learning: |

**Excellent ebook: http://www-stat.stanford.edu/~tibs/ElemStatLearn/ | **Excellent ebook: http://www-stat.stanford.edu/~tibs/ElemStatLearn/ | ||

**Classic ML Book: http://www.amazon.com/Pattern-Classification-2nd-Richard-Duda/dp/0471056693 | **Classic ML Book: http://www.amazon.com/Pattern-Classification-2nd-Richard-Duda/dp/0471056693 | ||

**Another ML Book (passed around in meetup): http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738 | **Another ML Book (passed around in meetup): http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738 | ||

+ | **Good ML Tutorials: http://www.autonlab.org/tutorials/ | ||

*Writeups on Optimization | *Writeups on Optimization | ||

**Gradient Descent/Conjugate Gradient: http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf | **Gradient Descent/Conjugate Gradient: http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf | ||

**Least Angle Regression: http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf | **Least Angle Regression: http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf | ||

*Python Linear Least Squares Fitting Routine: http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html | *Python Linear Least Squares Fitting Routine: http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html |

## Latest revision as of 11:36, 22 April 2010

### [edit] Overview

- Mike S talked about linear regression.
- Overview of linear least squares
- Talked about gradient descent
- Passed around some python code for doing least squares
- Talked about starting a linear algebra mini-course
- Talked about presenting stuff on SVMs at next meetup

### [edit] Details

- Some good books and references on linear regression/machine learning:
- Excellent ebook: http://www-stat.stanford.edu/~tibs/ElemStatLearn/
- Classic ML Book: http://www.amazon.com/Pattern-Classification-2nd-Richard-Duda/dp/0471056693
- Another ML Book (passed around in meetup): http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738
- Good ML Tutorials: http://www.autonlab.org/tutorials/

- Writeups on Optimization
- Gradient Descent/Conjugate Gradient: http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf
- Least Angle Regression: http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf

- Python Linear Least Squares Fitting Routine: http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html